This paper introduces an automated, geometry-focused framework designed for industrial part identification in metal additive manufacturing (AM), advancing real-world product development across diverse sectors. Relying on a recently published method, it employs a suitability score (SC) to quantitatively assess parts for industrial AM part identification using powder bed fusion – laser beam with metals (PBF-LB/M) and directed energy deposition – arc with metals (DED-Arc/M), evaluating manufacturability, adaptation effort, cost, and part complexity. Using part geometry as minimal input and additionally company-specific enterprise resource planning (ERP) data, it supports rapid, detailed assessments. It delivers comparative analysis of AM and conventional processes across multiple criteria, aiding industrial decision-making. Its modular architecture, with application programming interface (API) and web app integration, supports industrial adoption with customizable configurations. A prototype, validated through expert collaboration in industrial settings, enhances part selection and AM process efficiency. This novel framework for PBF-LB/M and DED-Arc/M integration rapidly screens large part portfolios and overcomes qualitative method limitations, enhancing industrial AM adoption.